Ying Wang
Assistant Professor
Campus
UTSC
CSB Appointment
Cross
Research Areas
Bioinformatics / Computational Biology, Genetics / Genomics, Quantitative Biology /Modelling, Systems Biology
Education
Ph.D., The University of Queensland, 2021
Master of Medicine, Sun Yat-Sen Univeristy, 2016
Bachelor of Medicine, Sun Yat-Sen Univeristy, 2013
Graduate Programs
Cell & Systems Biology
Academic or Administrative Appointments
Assistant Professor, Department of Biological Sciences, University of Toronto Scarborough (starting January 2027); primary graduate program appointment, Department of Cell & Systems Biology.
Research Description
The Wang laboratory develops statistical and computational methods to understand how genetic variation influences human disease across populations and over time. We integrate genome-wide association studies, rare-variant analyses, electronic health records and longitudinal biobank data to investigate disease susceptibility, onset, progression and clinical outcomes. A central goal is to move beyond conventional case-control genetics toward models that capture transitions between disease states and how genetic risks evolve across the life course.
Contact Information
Office: SW540
Lab: SW540
Email
Mailing Address
Department of Biological Sciences, UTSC
1265 Military Trail, SW 540
Toronto, ON M1C 1A4
Canada
Publications
2026
All of Us diversity and scale yield context-dependent improvements in polygenic prediction
Tsuo K, Shi Z, Ge T, Mandla R, Hou K, Ding Y, Pasaniuc B, Wang Y, Martin AR
2026, Nature Genetics, 10.1038/s41588-026-02734-4
2024
Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds
Wang Y, He Y, Shi Y, Qian DC, Gray KJ, Winn R, Martin AR
2024, The American Journal of Human Genetics, 10.1016/j.ajhg.2024.04.002
2023
Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology
Wang Y, Kanai M, Tan T, Kamariza M, Tsuo K, Yuan K, Zhou W, Okada Y, Huang H, Turley P, Atkinson EG, Martin AR
2023, Cell Genomics, 10.1016/j.xgen.2023.100408
Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
Wang Y, Namba S, Lopera E, Kerminen S, Tsuo K, Läll K, Kanai M, Zhou W, Wu K, Favé M, Bhatta L, Awadalla P, Brumpton B, Deelen P, Hveem K, Lo Faro V, Mägi R, Murakami Y, Sanna S, Smoller JW, Uzunovic J, Wolford BN, Zhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, Wang Y, Bhattacharya A, Zhao H, Namba S, Surakka I, Wolford BN, Lo Faro V, Lopera-Maya EA, Läll K, Favé M, Chapman SB, Karjalainen J, Kurki M, Mutaamba M, Partanen JJ, Brumpton BM, Chavan S, Chen T, Daya M, Ding Y, Feng YA, Gignoux CR, Graham SE, Hornsby WE, Ingold N, Johnson R, Laisk T, Lin K, Lv J, Millwood IY, Palta P, Pandit A, Preuss MH, Thorsteinsdottir U, Uzunovic J, Zawistowski M, Zhong X, Campbell A, Crooks K, de Bock GH, Douville NJ, Finer S, Fritsche LG, Griffiths CJ, Guo Y, Hunt KA, Konuma T, Marioni RE, Nomdo J, Patil S, Rafaels N, Richmond A, Shortt JA, Straub P, Tao R, Vanderwerff B, Barnes KC, Boezen M, Chen Z, Chen C, Cho J, Smith GD, Finucane HK, Franke L, Gamazon ER, Ganna A, Gaunt TR, Ge T, Huang H, Huffman J, Koskela JT, Lajonchere C, Law MH, Li L, Lindgren CM, Loos RJ, MacGregor S, Matsuda K, Olsen CM, Porteous DJ, Shavit JA, Snieder H, Trembath RC, Vonk JM, Whiteman D, Wicks SJ, Wijmenga C, Wright J, Zheng J, Zhou X, Awadalla P, Boehnke M, Cox NJ, Geschwind DH, Hayward C, Hveem K, Kenny EE, Lin Y, Mägi R, Martin HC, Medland SE, Okada Y, Palotie AV, Pasaniuc B, Sanna S, Smoller JW, Stefansson K, van Heel DA, Walters RG, Zöllner S, Martin AR, Willer CJ, Daly MJ, Neale BM, Willer C, Gamazon ER, Cox NJ, Surakka I, Okada Y, Martin AR, Hirbo J
2023, Cell Genomics, 10.1016/j.xgen.2022.100241
2022
Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores
Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR
2022, Annual Review of Biomedical Data Science, 10.1146/annurev-biodatasci-111721-074830